Hospital Services to Improve Nutritional Intake and Reduce Food Waste: A Systematic Review
Bibliographic record
Abstract
Background and Aims: Patients’ nutritional intake is a crucial issue in modern hospitals, where the high prevalence of disease-related malnutrition may worsen clinical outcomes. On the other hand, food waste raises concerns in terms of sustainability and environmental burden. We conducted a systematic review to ascertain which hospital services could overcome both issues. Methods: A systematic literature search following PRISMA guidelines was conducted across MEDLINE, Web of Science, and Scopus for randomised controlled trials (RCTs) and observational studies comparing the effect of hospital strategies on energy intake, protein intake, and plate/food waste. The quality of included studies was assessed using the Newcastle-Ottawa Scale for cohort studies and the Cochrane Risk of Bias tool from the Cochrane Handbook for Systematic Reviews of Interventions for RCTs. Results: Nineteen studies were included, assessing as many hospital strategies such as food service systems—including catering and room service—(n = 9), protected mealtimes and volunteer feeding assistance (n = 4), food presentation strategies (n = 3), nutritional counseling and education (n = 2), plant-based proteins meal (n = 1). Given the heterogeneity of the included studies, the results were narratively analysed. Conclusions: Although the results should be confirmed by prospective and large sample-size studies, the personalisation of the meal and efficient room service may improve nutritional intake while decreasing food waste. Clinical nutritionist staff—especially dietitians—may increase food intake reducing food waste through active monitoring of the patients’ nutritional needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".